Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the behavioral pattern of the dYdX RAT, which executes Python scripts from temporary directories with suppressed output. It looks for Python processes (identified by original filename) executing from common temporary directories while also suppressing output (e.g., redirecting to /dev/null or using CREATE_NO_WINDOW).
Hunts for modifications to RC initialization scripts. This could point to persistence
This rule detects the presence of environment variables used to control the malicious SANDWORM_MODE payload in CI environments or on local systems. The presence of these variables in a process command line is a strong indicator of an active infection or a workflow being manipulated by the malware's control logic.
Detects creation of backdoor accounts with high privileges such as root.
Detects Windows PE files that contain string indicators associated with the Diaoyu loader as reported in the Shadow Campaigns research. The rule keys on multiple distinctive strings, including references to WordPress themed artefacts, a GitHub raw content host, and Diaoyu specific markers, and requires an MZ header plus either two or more indicators or specific high confidence string pairs.
This rule detects Python processes that are executed with command-line arguments indicative of silent execution or output redirection to null devices, combined with execution from a temporary directory. This pattern was observed in the dYdX PyPI package compromise, where a RAT ran undetected using these techniques.
Detects when the BeyondTrust remote support client process ('bomgar-scc.exe' or 'bomgar-scc') spawns a shell or a common reconnaissance/downloader tool. This behavior is a strong indicator of post-exploitation activity related to vulnerabilities like CVE-2026-1731, where an attacker gains code execution and begins to explore the compromised system.
Hunts for unwarranted hardware additions through USB device connections. In an environment where USB devices are not permitted.
Detects creation of malicious init.d scripts for persistence
To accompany Hunting rule equivalent at
https://detections.ai/rules/019c91c0-b2e0-759d-a61c-78d08e1d3d99
To accompany Hunting rule equivalent at
https://detections.ai/rules/019c91c0-b2e0-759d-a61c-78d08e1d3d99
Identifies the loading of a Linux kernel module from a suspicious, non-standard directory such as /tmp, /dev/shm, /var/tmp, or a user's local hidden directory. Attackers often deploy rootkits and other kernel-level malware from these locations to evade detection and gain persistence. This technique is a prerequisite for kernel-level bypasses like those used by the 'Singularity' rootkit.
Detects successful content injection leading to process execution
Detects the presence or execution of known TeamPCP malware and infrastructure tools (proxy.sh, kube.py, pcpcat.py, and tunneling tools like FRPS/gost) based on their MD5 file hashes. The group uses these standardized payloads to automate their operations.
This detection identifies network connections from non-browser processes to known Large Language Model (LLM) API endpoints. While some connections may be legitimate developer or application traffic, unexpected processes connecting to these services could indicate unauthorized data exfiltration, corporate espionage, or misuse of AI services.
This detection identifies suspicious command-line pipelines commonly used in "ClickFix" attacks. It specifically searches for the use of 'curl' or 'wget' to download a payload that is immediately executed via a shell, often hidden within a subshell `$(...)` or piped directly to an interpreter. This "download and execute" pattern is a high-fidelity indicator of a successful ClickFix lure, where a user has been tricked into running a malicious command.
Hunts for unusual external SSH access patterns indicating Technique T1133
Hunts for unusual external SSH access patterns indicating Technique T1133
This rule detects outbound network connections from Ivanti EPMM appliances to known Out-of-Band (OOB) interaction services and tunneling platforms. This activity is often associated with automated reconnaissance and exploitation verification for vulnerabilities like CVE-2026-1281.
Detects modification of the VSCode settings.json file by a process other than VSCode itself. This could indicate an attempt to exploit vulnerabilities where an attacker tricks a user into applying a malicious configuration to achieve remote code execution.
Detects potential command injection attacks against AI agents through
suspicious prompt patterns or system command execution by AI processes.
suspicious prompt patterns or system command execution by AI processes.
Detects JavaScript-based drive-by compromise execution
Detects activity related to Network Discovery.
Here, one group of network commands has to match for the rule to trigger.
Here, one group of network commands has to match for the rule to trigger.
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